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Online multiple instance boosting for object detection 总被引:1,自引:0,他引:1
Zhiquan QiAuthor Vitae Yitian XuAuthor Vitae Laisheng WangAuthor Vitae Ye SongAuthor Vitae 《Neurocomputing》2011,74(10):1769-1775
Semi-supervised or unsupervised, incremental learning approaches based on online boosting are very popular for object detection. However, in the course of online learning, since the positive examples labelled by the current classifier may actually not be “correct”, the optimal weak classifier is unlikely to be selected by previous approaches. This would directly lead to a decline in algorithm performance. In this paper, we present an improved online multiple instance learning algorithm based on boosting (called OMILBoost) for object detection. It can pick out the real correct image patch around labelled example with high possibility and thus, avoid drifting problem effectively. Furthermore, our method shows high performance when dealing with partial occlusions. Effectiveness is experimentally demonstrated on six representative video sequences. 相似文献
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A generalized discriminative multiple instance learning (GDMIL) algorithm is presented to train the classifier in the condition of vague annotation of training samples GDMIL not only inherits the original MIL's capability of automatically weighting the instances in the bag according to their relevance to the concept but also integrates generative models using discriminative training. It is evaluated on the task of multimedia semantic concept detection using the development data set of TRECVID 2005. The experimental results show GDMIL outperforms the baseline systems trained on MIL with diverse density and expectation–maximization diverse density and the system without MIL. 相似文献
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Multiple instance learning (MIL) assigns a single class label to a bag of instances tailored for some real-world applications such as drug activity prediction.C... 相似文献
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如何检测数据集中的奇异值仍然是多元校正中的1个重要的问题.对于化学计量学研究者来说,找到1个普遍适用的方法仍然是1个重要的任务.本文的目的是介绍1种较新的基于自助法的奇异值检测方法.本法以内部学生化残差为基准,用自助法对相关变量进行估计,并采用刀切-自助法对估计值进行评价.它不要求回归模型的残差服从正态分布,因而适用于大部分回归分析中的奇异值检测.本文中采用烟草和玉米样本的近红外光谱数据对该法进行验证,结果表明,采用基于自助法的奇异值检测方法剔除奇异样品后,模型的预测误差减小15%,优于学生化残差-杠杆值法和稳健偏最小二乘法.我们还在玉米近红外光谱的基础上,进行了奇异样品数的模拟研究,并采用该法进行检验.结果表明,当奇异样品的数量少于总样品数的10%时,该方法的表现较其它2种方法好.所以,基于自助法的奇异值检测方法是1种有效的方法. 相似文献
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局部离群因子(LOF)是对过程数据的局部离群程度的定义,然而工业过程对数据异常检测的实时性要求高,要求出所有采样点的离群因子计算量较大。故本文对LOF算法进行相应的改进,采用k-近邻计算对象的局部可达密度,同时利用1种预处理采样点的方法CDC(Closest Distance to Center),通过计算每个点到中心点的距离先对采样点进行修剪,剔除大部分不可能是离群点的采样点,只需要计算剩余点改进的LOF值,从而提高离群点检测的效率。最终通过对TE过程数据仿真,说明在保证离群点检测准确性的情况下,相比于LOF缩短了算法运行的时间。 相似文献
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Multivariate outlier identification requires the choice of reliable cut-off points for the robust distances that measure the discrepancy from the fit provided by high-breakdown estimators of location and scatter. Multiplicity issues affect the identification of the appropriate cut-off points. It is described how a careful choice of the error rate which is controlled during the outlier detection process can yield a good compromise between high power and low swamping, when alternatives to the Family Wise Error Rate are considered. Multivariate outlier detection rules based on the False Discovery Rate and the False Discovery Exceedance criteria are proposed. The properties of these rules are evaluated through simulation. The rules are then applied to real data examples. The conclusion is that the proposed approach provides a sensible strategy in many situations of practical interest. 相似文献
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Roth V 《Neural computation》2006,18(4):942-960
The problem of detecting atypical objects or outliers is one of the classical topics in (robust) statistics. Recently, it has been proposed to address this problem by means of one-class SVM classifiers. The method presented in this letter bridges the gap between kernelized one-class classification and gaussian density estimation in the induced feature space. Having established the exact relation between the two concepts, it is now possible to identify atypical objects by quantifying their deviations from the gaussian model. This model-based formalization of outliers overcomes the main conceptual shortcoming of most one-class approaches, which, in a strict sense, are unable to detect outliers, since the expected fraction of outliers has to be specified in advance. In order to overcome the inherent model selection problem of unsupervised kernel methods, a cross-validated likelihood criterion for selecting all free model parameters is applied. Experiments for detecting atypical objects in image databases effectively demonstrate the applicability of the proposed method in real-world scenarios. 相似文献
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针对Mohemmed等新近提出的基于粒子群优化(PSO)算法的离群点检测方法(MOHEMMED A,ZHANG M,BROWNE W.Particle swarm optimisation for outlier detection[C]∥GECCO'10:Proceedings of the 12th AnnualConfernce on Genetic and Evolutionary Computation.Oregon,Portland:ACM,2010:83-84)可能出现适应值和相应数据对象的离群度不匹配的不合理现象,分析了存在这种现象的原因,并提出一种改进的适应值函数.新的适应值调整了对不合理邻域半径估值的惩罚力度,从而弱化粒子适应值和对象离群度之间的偏差;算法在解空间范围内搜索近似最优粒子,以确定合适的邻域半径估值;最终基于该半径估值衡量各数据对象的离群度.通过对若干UGI数据案的实验表明,采用新的适应值函数的离群检测算法优于原有方法和LOF方法.所提算法不仅解决了上述存在的问题,离群点检测效果也更突出,这表明合理定义适应值函数有助于提高算法的检测质量. 相似文献
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随着离群点检测技术的深入研究和广泛应用,越来越多的优秀算法被提出来,然而,现有的离群点检测技术的评价仍然沿用传统分类算法的测量指标,存在着评价指标单一、适应性差的问题。针对这些问题,提出了一类高真正率指标(HT_AUC)和二类低假正率指标(LF_AUC)。首先,整理常用的离群点检测评价指标,分析其优缺点和适用场景;然后,在已有的曲线下面积(AUC)方法的基础上,分别针对高真正率(TPR)要求和低假正率(FPR)要求,提出了一类高真正率指标和二类低假正率指标,为离群点检测算法的效果评价和量化集成提供了更合适的指标。在真实数据集上的实验结果表明,与传统评价指标的相比,所提出的方法更能满足一类高真正率和二类低假正率要求。 相似文献
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随着离群点检测技术的深入研究和广泛应用,越来越多的优秀算法被提出来,然而,现有的离群点检测技术的评价仍然沿用传统分类算法的测量指标,存在着评价指标单一、适应性差的问题。针对这些问题,提出了一类高真正率指标(HT_AUC)和二类低假正率指标(LF_AUC)。首先,整理常用的离群点检测评价指标,分析其优缺点和适用场景;然后,在已有的曲线下面积(AUC)方法的基础上,分别针对高真正率(TPR)要求和低假正率(FPR)要求,提出了一类高真正率指标和二类低假正率指标,为离群点检测算法的效果评价和量化集成提供了更合适的指标。在真实数据集上的实验结果表明,与传统评价指标的相比,所提出的方法更能满足一类高真正率和二类低假正率要求。 相似文献
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Uncertain data are common due to the increasing usage of sensors, radio frequency identification(RFID), GPS and similar devices for data collection. The causes of uncertainty include limitations of measurements, inclusion of noise, inconsistent supply voltage and delay or loss of data in transfer. In order to manage, query or mine such data, data uncertainty needs to be considered. Hence,this paper studies the problem of top-k distance-based outlier detection from uncertain data objects. In this work, an uncertain object is modelled by a probability density function of a Gaussian distribution. The naive approach of distance-based outlier detection makes use of nested loop. This approach is very costly due to the expensive distance function between two uncertain objects. Therefore,a populated-cells list(PC-list) approach of outlier detection is proposed. Using the PC-list, the proposed top-k outlier detection algorithm needs to consider only a fraction of dataset objects and hence quickly identifies candidate objects for top-k outliers. Two approximate top-k outlier detection algorithms are presented to further increase the efficiency of the top-k outlier detection algorithm.An extensive empirical study on synthetic and real datasets is also presented to prove the accuracy, efficiency and scalability of the proposed algorithms. 相似文献
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With increasing variation in parametric data, it is necessary to adopt statistical means and correlations that consider other process parameters. Determining an appropriate threshold is difficult because of the several orders of magnitude variation in fault-free I/sub DDQ/. Therefore, it is necessary to use secondary information to identify outliers. This article proposed a combination of two I/sub DDQ/ test metrics for screening outlier chips by exploiting wafer-level spatial correlation. No single metric alone suffices to screen all outliers. The addition of a secondary metric also comes at the risk of additional yield loss. Maintaining stringent process control proves to be challenging for deer-submicron technologies. Therefore, understanding underlying process variables and their impact on test parameters are crucial for yield requirements. As I/sub DDQ/ test loses its effectiveness, it becomes necessary to correlate multiple test metrics, and a combination of multiple outlier screening methods might be necessary. A combination of CR and NCR with other test parameters can be useful for screening low-reliability chips, and an analysis of wafer patterns can be useful in reducing the number of required vector pairs. 相似文献
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Shahrjooihaghighi Aliasghar Frigui Hichem 《Journal of Intelligent Information Systems》2022,59(1):45-69
Journal of Intelligent Information Systems - We propose a local feature selection method for the Multiple Instance Learning (MIL) framework. Unlike conventional feature selection algorithms that... 相似文献
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基于高效多示例学习的目标跟踪 总被引:1,自引:0,他引:1
基于多示例学习(MIL)的跟踪算法能在很大程度上缓解漂移问题。然而,该算法的运行效率相对较低,精度也有待提高,这是由于MIL算法采用的强分类器更新策略效率不高,以及分类器更新速度与目标外观变化速度不一致引起的。为此提出一种新的强分类器更新策略,以大幅提升MIL算法的运行效率;同时提出一种动态更新分类器学习率的机制,使更新后的分类器更符合目标的外观,提高跟踪算法的精度。通过实验将该算法和MIL算法以及基于加权多示例学习的跟踪算法(WMIL)进行对比,实验结果表明,所提出算法的运行效率和跟踪精度都是三者中最好的,在背景中没有与被跟踪目标外观相似的干扰物体存在时有较好的跟踪优势。 相似文献